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arXiv · 2309.07566

Speech-to-Speech Translation with Discrete-Unit-Based Style Transfer

Yongqi Wang·Jionghao Bai·Rongjie Huang·Ruiqi Li·Zhiqing Hong·Zhou Zhao
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Abstract

Direct speech-to-speech translation (S2ST) with discrete self-supervised representations has achieved remarkable accuracy, but is unable to preserve the speaker timbre of the source speech. Meanwhile, the scarcity of high-quality speaker-parallel data poses a challenge for learning style transfer during translation. We design an S2ST pipeline with style-transfer capability on the basis of discrete self-supervised speech representations and codec units. The acoustic language model we introduce for style transfer leverages self-supervised in-context learning, acquiring style transfer ability without relying on any speaker-parallel data, thereby overcoming data scarcity. By using extensive training data, our model achieves zero-shot cross-lingual style transfer on previously unseen source languages. Experiments show that our model generates translated speeches with high fidelity and speaker similarity. Audio samples are available at http://stylelm.github.io/ .

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BibTeXRIS

Yongqi Wang, Jionghao Bai, Rongjie Huang, Ruiqi Li, Zhiqing Hong, Zhou Zhao. 2023-09-14. Speech-to-Speech Translation with Discrete-Unit-Based Style Transfer. https://arxiv.org/abs/2309.07566

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